An Expert System for Penalty Apportioning in Cases of Examination Malpractice

Abstract

Punishment apportioning in cases of examination malpractice in institutions of learning is often affected by biases such as gender, age, and socioeconomic status. These biases frequently lead to inconsistencies, unfairness, and injustice, thereby undermining the integrity of the examination system. Despite the existence of clear punitive guidelines for assigning penalties to erring students in most higher institutions, these guidelines are still subjectively enforced, often influenced by various biases. To mitigate this problem, it is important to develop a reliable and objective method for determining penalties for examination malpractice. This study introduces an innovative expert system that applies existing punitive knowledge and reasoning to determine penalties in a more objective and reliable manner. By adopting a consistent, rule-based approach, the system ensures fair and consistent outcomes, thus addressing the challenge of subjective human judgment. The study details the system’s development process, from conceptualization to implementation. A knowledge base containing 48 rules was designed for the expert system. ES-Builder 3.0, an expert development tool, was used to build the model. Using PHP and MySQL, ES-Builder 3.0 provides a web-based Expert System Shell (ESS) that makes decisions based on a decision tree. A dataset of examination malpractice guidelines from the Federal Cooperative College, Ibadan (FCCIb) was used to evaluate the model’s efficacy. The results suggest that the expert system is objective and free from bias, and it is therefore recommended for penalty apportioning in cases of examination malpractice.